Image("../figs/bubbles_ice_core.png", width = 650)
Image("../figs/2580_02.png", width = 100)
Image("../figs/2580_02det.png", width = 850)
The process of densification is temperature and accumulation dependant.
Image("../figs/dome_c_density_herron.png", width = 700)
| Description of medium | Density Range $\left[ \text{kgm}^{-3} \right]$ |
|---|---|
| New snow | 50-70 |
| Settled snow | 200-300 |
| Wind packed snow | 350-400 |
| Firn | 400-830 |
| Glacial Ice | 830-920 |
$s = 1-\frac{\rho}{\rho_{\text{ice}}}$
Porosity is the interstitial space between ice crystals. It can be referred to as open porosity if pores are still connected with the atmosphere, or closed if pores have been closed-off.
${\overline{\rho}}_{co} = {\left( \frac{1}{\rho_{ice}} + 6.95 \times 10^{-7}T-4.3 \times 10^{-5} \right)}^{-1} $
The average density at which the bubbles are close-off. Mostly dependant on temperature (and wind).
The depth at which all air has been occluded into bubbles.
The depth at which vertical gas transport has ceased. Likely due to high-density layers above the close-off depth. There is still open porosity below this depth and often it can be pumped
Image("../figs/buizert2013_densif_stages.png", width = 700)
Image("../figs/herron_title.png", width = 800)
rho_ice = 917.0
data_ngrip = np.genfromtxt("./NGRIP_density.txt", delimiter=",", skip_header=2)
z_ngrip = data_ngrip[:,0]
rho_ngrip = data_ngrip[:,1]
logrho_ngrip = np.log(rho_ngrip/(rho_ice - rho_ngrip))
plt.figure(2)
plt.plot(rho_ngrip, z_ngrip, "r.")
plt.twiny()
plt.plot(logrho_ngrip, z_ngrip, ".")
plt.ylim([120,0])
plt.ylabel("Depth [m]")
plt.xlabel("Density [kgm-3]")
plt.title("NGRIP")
<matplotlib.text.Text at 0x10e96f990>
Image("../figs/grip_herron.png", width = 800)
Image("../figs/buizer2013_sites.png", width = 900)
In Absalon you will find the following data sets:
Use the boundary conditions given in the table of Buizert et al to run the H-L model for every site in the list. Calculate the close-off depth and the age of the bubbles at this depth.
Your results are unlikely to fit every core site very well. Implement fudge parameters f0, f1 in your code in front of the activation energies and tune them so you fit the data sets in a least squares sense.
In the figure below you are given a plot of measured density versus depth for a shallow core from North Greenland. Use the Herron and Langway model in order to model the density by optimizing the fudge factors in the fron of the activation energy coeeficients $K_o, K_1$
When you have a pair of fudge coefficients $f_0, f_1$ calculate the following:
In the second figure you can see the isotopic profile for the NEEM ice core. The $\delta^{18}\text{O}$ are found in the file neem_megafile.txt uploaded in absalon. In the file you can also find information on the age, annual layer thickness and accumulation at every depth with a resolution of 55 cm.
Using th isotopic profile and assuming an isotope - temperature sensitivity $\frac{\text{d}\delta^{18}\text{O}}{\text{d}T} = 0.67 \text{ permile C}^{-1}$, calculate the past temperature for all the core. Present your results versus depth as well as verus age.
Use the accumulation column in the neemmegafile and combined with the temperatures you calculated in the previous step calculate the close-off depth as well as $\Delta{\text{age}}$ for every point of the core. Present your results versus depth and age.
Image("../figs/neemS2007_density.png", width = 900)
Image("../figs/neem_bags_d18.png", width = 950)